An effective class of estimators for population mean estimation in successive sampling using simulation approach

Abstract

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others.

Keywords:

• Generalized class
• two occasion sampling and optimum replacement policy

Mathematical subject classification:

• 62D05

Disclosure statement

No potential conflict of interest was reported by the author(s).